Adaptive and Natural Computing Algorithms pp 218-221
Offspring Selection: A New Self-Adaptive Selection Scheme for Genetic Algorithms
In terms of goal orientedness, selection is the driving force of Genetic Algorithms (GAs). In contrast to crossover and mutation, selection is completely generic, i.e. independent of the actually employed problem and its representation. GA-selection is usually implemented as selection for reproduction (parent selection). In this paper we propose a second selection step after reproduction which is also absolutely problem independent. This self-adaptive selection mechanism, which will be referred to as offspring selection, is closely related to the general selection model of population genetics. As the problem- and representation-specific implementation of reproduction in GAs (crossover) is often critical in terms of preservation of essential genetic information, offspring selection has proven to be very suited for improving the global solution quality and robustness concerning parameter settings and operators of GAs in various fields of applications. The experimental part of the paper discusses the potential of the new selection model exemplarily on the basis of standardized real-valued test functions in high dimensions.
Unable to display preview. Download preview PDF.
- Cavicchio, D.J. (1970) Adaptive Search using Simulated Evolution. Unpublished doctoral dissertation, University of Michigan, Ann ArborGoogle Scholar
- De Jong, K.A. (1975) An Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD. Thesis. University of MichiganGoogle Scholar
- Goldberg, D. E. (1989) Genetic Alogorithms in Search, Optimization and Machine Learning, Addison Wesley LongmanGoogle Scholar
- Affenzeller, M., Wagner S. (2004): SASEGASA: A new generic parallel evolutionary algorithm for achieving highest quality results. Journal of Heuristics-Special Issue on New Advances on Parallel Meta-Heuristics for Complex Problems, vol. 10: 239–263Google Scholar
- Wagner S., Affenzeller M. (2004): HeuristicLab-A generic and extensible optimization environment. Proceedings of IC ANNGA 2005Google Scholar
- Dumitrescu, D., Lazzerini B, Jain L.C., Dumitrescu, A. (2000): Evolutionary computation. CRC PressGoogle Scholar
- Potter, M.A., De Jong K. (1994): A cooperative coevolutionary approach to function optimization. In: Parallel Problem Solving from Nature — PPSN III, pp. 249–257Google Scholar
- Hiroyasu, T., Miki M., Hamasaki M, Tanimura, Y. (2000): A new model of distributed genetic algorithm for cluster systems: Dual individual DGA. High Performance Computing, Lecture Notes in Computer Sicence 1940, pp.374–383Google Scholar
- Takahashi, O., Kita, H., Kobayashi S. (1999): A distance dependent alternation model on real-coded genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics 24(4): 619–624Google Scholar
- Winkler S., Affenzeller M., Wagner S. (2004): Identifying nonlinear model structures using genetic programming techniques. In: Proceedings of the European Meeting on Cybernetics and Systems Research-EMCSR 2004, pp. 689–694Google Scholar